SC RNA-Seq: Checking for duplicates with dupRadar
Libraries required
library(dupRadar)
library(biomaRt)
library(parallel)
library(viridis)
library(dplyr)Bam files (marked duplicates)
bamFiles <- list.files(path = "./input", pattern = ".bam$", full.names = T)
names(bamFiles) <- as.character(unlist(sapply(bamFiles, function(x) strsplit(x, "\\.|\\/")[[1]][4])))Analyze duplicates
dm <- NULL
if (!file.exists("./output/analyzedDuplicates.rds")) {
dm <- lapply(bamFiles, function(x) {
a <- analyzeDuprates(bam = x, gtf = "./input/gencode.vM18.chr_patch_hapl_scaff.annotation.gff3",
stranded = 0, paired = F, threads = detectCores() - 1)
# removing versions from gene IDs
a$ID <- as.character(sapply(as.character(a$ID), function(y) strsplit(y, "\\.")[[1]][1]))
return(a)
})
saveRDS(object = dm, file = "./output/analyzedDuplicates.rds")
} else {
dm <- readRDS("./output/analyzedDuplicates.rds")
}Plotting and interpretation
The number of reads per base assigned to a gene in an ideal RNA-Seq data set is expected to be proportional to the abundance of its transcripts in the sample. For lowly expressed genes we expect read duplication to happen rarely by chance, while for highly expressed genes - depending on the total sequencing depth - we expect read duplication to happen often.
A good way to learn if a dataset is following this trend is by relating the normalized number of counts per gene (RPK, as a quantification of the gene expression) and the fraction represented by duplicated reads.
A duprate plot (blue cloud)
for (i in seq_along(dm)) {
name <- names(dm)[i]
cat("\n \n")
cat(paste("###", name))
cat("\n \n")
duprateExpDensPlot(DupMat = dm[[i]], pal = viridis(n = 1000), main = name)
cat("\n \n")
}PND15_11_B1
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Duprate Boxplot
The duprateExpBoxplot plot shows the range of the duplication rates at 5% bins (default) along the distribution of RPK gene counts. The x-axis displays the quantile of the RPK distribution, and the average RPK of the genes contained in this quantile.
for (i in seq_along(dm)) {
name <- names(dm)[i]
cat("\n \n")
cat(paste("###", name))
cat("\n \n")
duprateExpBoxplot(DupMat = dm[[i]], main = name)
cat("\n \n")
}PND15_11_B1
PND15_12_B1
PND15_18_B2
PND15_20_B2
PND15_21_B2
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Read counts expression
for (i in seq_along(dm)) {
name <- names(dm)[i]
cat("\n \n")
cat(paste("###", name))
cat("\n \n")
readcountExpBoxplot(DupMat = dm[[i]])
cat("\n \n")
}PND15_11_B1
PND15_12_B1
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Read counts expression histogram
for (i in seq_along(dm)) {
name <- names(dm)[i]
cat("\n \n")
cat(paste("###", name))
cat("\n \n")
expressionHist(DupMat = dm[[i]])
cat("\n \n")
}PND15_11_B1
PND15_12_B1
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Comparison of multi-mapping RPK and uniquely-mapping RPK
for (i in seq_along(dm)) {
name <- names(dm)[i]
cat("\n \n")
cat(paste("###", name))
cat("\n \n")
plot(log2(dm[[i]]$RPK), log2(dm[[i]]$RPKMulti), xlab = "Reads per kb (uniquely mapping reads only)",
ylab = "Reads per kb (all including multimappers, non-weighted)")
cat("\n \n")
}PND15_11_B1
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Connection between possible PCR artefacts and GC content
## set up biomart connection for mouse (needs internet connection)
ensm <- useMart("ensembl")
ensm <- useDataset("mmusculus_gene_ensembl", mart = ensm)
## get a table which has the gene GC content for the IDs that have been used to
## generate the table
tr <- getBM(attributes = c("ensembl_gene_id", "percentage_gene_gc_content"), values = TRUE,
mart = ensm)
## create a GC vector with IDs as element names
mgi.gc <- tr$percentage_gene_gc_content
names(mgi.gc) <- tr$ensembl_gene_idCheck distribution of annotated gene GC content (in %)
for (i in seq_along(dm)) {
name <- names(dm)[i]
cat("\n \n")
cat(paste("###", name))
cat("\n \n")
## using dm duplication matrix that comes with the package add GC content to our
## demo data and keep only subset for which we can retrieve data
keep <- dm[[i]]$ID %in% tr$ensembl_gene_id
dm.gc <- dm[[i]][keep, ]
dm.gc$gc <- mgi.gc[dm.gc$ID]
boxplot(dm.gc$gc, main = "Gene GC content", ylab = "% GC")
cat("\n \n")
}PND15_11_B1
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Compare the dependence of duplication rate on expression level independently for below and above median GC genes
for (i in seq_along(dm)) {
name <- names(dm)[i]
cat("\n \n")
cat(paste("###", name))
cat("\n \n")
keep <- dm[[i]]$ID %in% tr$ensembl_gene_id
dm.gc <- dm[[i]][keep, ]
dm.gc$gc <- mgi.gc[dm.gc$ID]
par(mfrow = c(1, 2))
## below median GC genes
duprateExpDensPlot(dm.gc[dm.gc$gc <= 45, ], main = "below median GC genes")
## above median GC genes
duprateExpDensPlot(dm.gc[dm.gc$gc >= 45, ], main = "above median GC genes")
cat("\n \n")
}PND15_11_B1
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References
report::cite_packages(sessionInfo()) - Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.5. https://CRAN.R-project.org/package=dplyr
- Julien Barnier (2021). rmdformats: HTML Output Formats and Templates for 'rmarkdown' Documents. R package version 1.0.1. https://CRAN.R-project.org/package=rmdformats
- Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Steffen Durinck, Paul T. Spellman, Ewan Birney and Wolfgang Huber, Nature Protocols 4, 1184-1191 (2009).
- R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
- Sergi Sayols, Denise Scherzinger and Holger Klein (2016): dupRadar: a Bioconductor package for the assessment of PCR artifacts in RNA-Seq data. BMC Bioinformatics, 17:428, doi:10.1186/s12859-016-1276-2
- Simon Garnier, Noam Ross, Robert Rudis, Antônio P. Camargo, Marco Sciaini, and Cédric Scherer (2021). Rvision - Colorblind-Friendly Color Maps for R. R package version 0.4.0.
- Simon Garnier, Noam Ross, Robert Rudis, Antônio P. Camargo, Marco Sciaini, and Cédric Scherer (2021). Rvision - Colorblind-Friendly Color Maps for R. R package version 0.6.0.
- Yihui Xie (2021). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.33.SessionInfo
devtools::session_info() %>%
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